This project addresses the need for better methods for deciphering the glycosylation of proteins in clinical samples. Glycosylation is an important modifier of protein structure and function and contributes to disease processes. But we currently know little about the glycosylation of most proteins. The current methods for probing glycans on proteins are not suitable for meeting this need, as they require much material and many processing steps. Here we propose and practical approach to probing protein glycosylation that will provide: 1) the ability to obtain structural and compositional information with limited sample usage; 2) the ability to precisely compare glycan levels between samples; and 3) ready translation into a clinical assay. We will achieve this goal through novel informatics techniques that facilitate the combined use of mass spectrometry (MS) and lectin binding for studying glycans. Phase II will focus on glycoprotein biomarkers of pancreatic cancer. MS provides the monosaccharide compositions of glycans and some sequence information, but it leaves ambiguities about sequence or linkage variants. Likewise, lectins can give precise measurements of specific structures using small amounts of sample, but they do not provide a complete picture of each glycan. We predict that quantitatively integrating the two types of information will give more accurate information than either method alone. We will quantitatively link lectin experiments to MS experiments using the common language of motifs - substructures of glycans. In Aim 1, we will develop an algorithm for identifying what glycan motifs are most likely present in a sample based on lectin binding. In Aim 2, we will develop tools for integrating lectin and MS data and will use the method to characterize and compare the glycans of three different purified glycoproteins. We will determine whether the linking of MS and lectin data provides more complete information than either method alone, with limited sample consumption and the ability to make precise comparisons between samples.